Minor updates

README.md

@@ -76,6 +76,9 @@ asset-class-comparison/
     └── fd_utils.py       # Fixed deposit calculation utilities
 ```
 
+# Ideas for future improvements
+- [ ] Add credit rating of each stock
+
 ## Contributing
 
 Contributions are welcome! Please feel free to submit a Pull Request.

app.py

@@ -11,319 +11,624 @@ from utils.currency_utils import get_usd_sgd_rate
 from utils.fd_utils import calculate_fd_returns
 from utils.hdb_utils import calculate_hdb_returns
 
+def fetch_stock_daily(symbol, start_date, end_date):
+    print (f"Fetching stock data for {symbol} from {start_date} to {end_date}")
+    start_date_dt = pd.Timestamp(start_date)
+    end_date_dt = pd.Timestamp(end_date)
+    try:
+        folder = "notebooks/stock_data/"
+        data = pd.read_csv(
+            f"{folder}{symbol}.csv", 
+            parse_dates=["Date"]
+            )
+        print (f'Date range: {data["Date"].min()} - {data["Date"].max()}')
+        print (f"start_date_dt: {start_date_dt}, end_date_dt: {end_date_dt}")
+        data = data[(data["Date"] >= start_date_dt) & (data["Date"] <= end_date_dt)]
+        data.set_index("Date", inplace=True)
+        data = data["Close"]
+        return data
+    except FileNotFoundError:
+        print(f"File {folder}{symbol}.csv not found.")
+        return None
+    
 
 print("Starting the app ...")
 
-# Sanity check on the yfinance_utils
-print("Sanity check on the yfinance_utils ...")
-# print(fetch_yfinance_daily("MSFT", "2020-01-01", "2020-01-03"))
-data = yf.download("MSFT", "2020-01-01", "2020-01-03")
-print(data.head())
-
 # Set page config
-st.set_page_config(page_title="Asset Class Comparison", layout="wide")
+st.set_page_config(
+    page_title="Asset Class Comparison", 
+    layout="wide",
+    menu_items={
+        'Get Help': 'https://github.com/yourusername/asset-class-comparison',
+        'Report a bug': 'https://github.com/yourusername/asset-class-comparison/issues',
+        'About': 'Compare the performance of different asset classes over time'
+    }
+)
 
 # Title and description
 st.title("Asset Class Performance Comparison")
-st.write("Compare the performance of different asset classes over time")
-# st.write("Note: Cryptocurrencies (BTC, ETH, SOL, DOGE) are highly volatile and should be considered high-risk investments")
-
-# Sidebar for user inputs
-st.sidebar.header("Investment Parameters")
-currency = st.sidebar.selectbox("Display Currency", ["USD", "SGD"], index=0)
-initial_investment = st.sidebar.number_input(f"Initial Investment Amount ({currency})", min_value=1000, value=10000, step=1000)
-start_date = st.sidebar.date_input("Start Date", value=datetime.now() - timedelta(days=365*25))
-user_end_date = st.sidebar.date_input("End Date", value=datetime.now())
-
-fd_rate = st.sidebar.number_input("Fixed Deposit Rate (%)", min_value=0.0, value=2.9, step=0.1) / 100
-use_log_scale = st.sidebar.checkbox("Use Log Scale", value=True)
-
-# Calculate and display investment period
-investment_days = (user_end_date - start_date).days
-investment_years = investment_days / 365
-st.write(f"Investment Period: {investment_days} days ({investment_years:.1f} years)")
-
-# Asset selection
-selected_assets = st.sidebar.multiselect(
-    "Select Assets to Compare",
-    [
-        "Fixed Deposit",
-        "HDB",
-        "Gold",
-        "SGS Bonds",
-        "US Treasury Bonds",
-        "NASDAQ Composite",
-        "NASDAQ Large Cap",
-        "NASDAQ 100",
-        "S&P 500",
-        "Dow Jones",
-        "Microsoft",
-        "Google",
-        "Nvidia",
-        "Apple",
-        "Amazon",
-        "Tesla",
-        "Netflix",
-        "Meta",
-        "Bitcoin",
-        "Ethereum",
-        "Solana",
-        "Dogecoin",
-    ],
-    default=[
-        "Fixed Deposit", 
-        "HDB",
-        "Gold", 
-        "US Treasury Bonds", "SGS Bonds", 
-        "S&P 500", "Dow Jones", "NASDAQ Composite", #"NASDAQ Large Cap", "NASDAQ 100",
-        "Microsoft", "Google", "Nvidia",
-        "Bitcoin"  
+
+# Initialize session state to track which tab is active
+if 'active_tab' not in st.session_state:
+    st.session_state.active_tab = 0
+
+# Get URL parameters and set the active tab accordingly
+if "tab" in st.query_params:
+    tab_value = st.query_params["tab"].lower()
+    if tab_value in ["readme", "2", "calculator", "formulas", "guide"]:
+        st.session_state.active_tab = 1
+    elif tab_value in ["1", "comparison", "main", "invest", "investment"]:
+        st.session_state.active_tab = 0
+
+# Create a tab selector
+tab_options = ["Investment Comparison", "README & Formulas"]
+selected_tab = st.radio("Select Tab:", tab_options, horizontal=True, index=st.session_state.active_tab, label_visibility="collapsed")
+
+# Update session state and URL when tab changes
+if selected_tab == "Investment Comparison" and st.session_state.active_tab != 0:
+    st.session_state.active_tab = 0
+    st.query_params["tab"] = "comparison"
+    st.rerun()
+elif selected_tab == "README & Formulas" and st.session_state.active_tab != 1:
+    st.session_state.active_tab = 1
+    st.query_params["tab"] = "readme"
+    st.rerun()
+    
+# Display URL tips
+with st.expander("URL Tips 🔗"):
+    st.markdown("""
+    You can use URL parameters to directly navigate to specific tabs:
+    
+    * Main investment comparison: `?tab=1` or `?tab=comparison` 
+    * README & Calculator: `?tab=2` or `?tab=readme`
+    
+    Example: `http://yourapp.com/?tab=readme`
+    
+    These links can be bookmarked or shared to go directly to a specific tab.
+    
+    You can also set the tab programmatically in your app:
+    ```python
+    # To switch to the README tab
+    st.query_params["tab"] = "readme"
+    
+    # To clear all parameters
+    st.query_params.clear()
+    ```
+    """)
+
+# Display content based on the selected tab
+if st.session_state.active_tab == 0:
+    # INVESTMENT COMPARISON TAB
+    st.write("Compare the performance of different asset classes over time")
+    # st.write("Note: Cryptocurrencies (BTC, ETH, SOL, DOGE) are highly volatile and should be considered high-risk investments")
+    
+    # Sidebar for user inputs
+    st.sidebar.header("Investment Parameters")
+    currency = st.sidebar.selectbox("Display Currency", ["USD", "SGD"], index=0)
+    initial_investment = st.sidebar.number_input(f"Initial Investment Amount ({currency})", min_value=1000, value=10000, step=1000)
+    start_date = st.sidebar.date_input("Start Date", value=datetime.now() - timedelta(days=365*25))
+    user_end_date = st.sidebar.date_input("End Date", value=datetime.now())
+    
+    fd_rate = st.sidebar.number_input("Fixed Deposit Rate (%)", min_value=0.0, value=2.9, step=0.1) / 100
+    use_log_scale = st.sidebar.checkbox("Use Log Scale", value=True)
+    
+    # Calculate and display investment period
+    investment_days = (user_end_date - start_date).days
+    investment_years = investment_days / 365
+    st.write(f"Investment Period: {investment_days} days ({investment_years:.1f} years)")
+    
+    # Asset selection
+    selected_assets = st.sidebar.multiselect(
+        "Select Assets to Compare",
+        [
+            "Fixed Deposit",
+            "HDB",
+            "Gold",
+            "SGS Bonds",
+            "US Treasury Bonds",
+            "NASDAQ Composite",
+            "NASDAQ-100",
+            "Invesco QQQ Trust",
+            "Fidelity NASDAQ Composite Index ETF",
+            "Invesco NASDAQ 100 ETF",
+            "S&P 500",
+            "Dow Jones",
+            "Microsoft",
+            "Google (Alphabet)",
+            "Nvidia",
+            "Apple",
+            "Amazon",
+            "Tesla",
+            "Netflix",
+            "Meta (Facebook)",
+            "Bitcoin",
+            "Ethereum",
+            "Solana",
+            "Dogecoin",
+        ],
+        default=[
+            "Fixed Deposit", 
+            "HDB",
+            "Gold", 
+            "US Treasury Bonds", 
+            "SGS Bonds", 
+            "S&P 500", 
+            "Dow Jones", 
+            "NASDAQ Composite",
+            "Microsoft", 
+            "Google (Alphabet)", 
+            "Meta (Facebook)",
+            "Nvidia",
+            "Bitcoin"  
         ]
-)
+    )
+
+    # Today's date for reference
+    today = datetime.now().date()
+    today = today# - timedelta(days=1) # Temporarily set to yesterday
+
+    # usd_to_sgd = get_usd_sgd_rate() if currency == "SGD" else 1.0
+    usd_to_sgd = 1.30 # Temporarily set to 1.30 to test the app
+    currency_symbol = "$" if currency == "USD" else "S$"
+
+    # Create a dictionary of tickers for yfinance
+    tickers = {
+        "Gold": "GLD",
+        # "HDB": "A12.SI",
+        "SGS Bonds": "A35.SI",  # Nikko AM SGD Investment Grade Corporate Bond ETF
+        "US Treasury Bonds": "TLT",  # iShares 20+ Year Treasury Bond ETF
+        "NASDAQ Composite": "^IXIC",
+        "NASDAQ Large Cap": "^NDX",
+        "NASDAQ 100": "^NDX",
+        "S&P 500": "^GSPC",
+        "Dow Jones": "^DJI",
+        "Microsoft": "MSFT",
+        "Google (Alphabet)": "GOOGL",
+        "Nvidia": "NVDA",
+        "Apple": "AAPL",
+        "Amazon": "AMZN",
+        "Tesla": "TSLA",
+        "Netflix": "NFLX",
+        "Meta (Facebook)": "META",
+        "Bitcoin": "BTC-USD",
+        "Ethereum": "ETH-USD",
+        "Solana": "SOL-USD",
+        "Dogecoin": "DOGE-USD",
+    }
 
-# Today's date for reference
-today = datetime.now().date()
-
-usd_to_sgd = get_usd_sgd_rate() if currency == "SGD" else 1.0
-currency_symbol = "$" if currency == "USD" else "S$"
-
-# Create a dictionary of tickers for yfinance
-tickers = {
-    "Gold": "GC=F",
-    "HDB": "A12.SI",
-    "SGS Bonds": "A35.SI",  # Nikko AM SGD Investment Grade Corporate Bond ETF
-    "US Treasury Bonds": "TLT",  # iShares 20+ Year Treasury Bond ETF
-    "NASDAQ Composite": "^IXIC",
-    "NASDAQ Large Cap": "^NDX",
-    "NASDAQ 100": "^NDX",
-    "S&P 500": "^GSPC",
-    "Dow Jones": "^DJI",
-    "Microsoft": "MSFT",
-    "Google": "GOOGL",
-    "Nvidia": "NVDA",
-    "Apple": "AAPL",
-    "Amazon": "AMZN",
-    "Tesla": "TSLA",
-    "Netflix": "NFLX",
-    "Meta": "META",
-    "Bitcoin": "BTC-USD",
-    "Ethereum": "ETH-USD",
-    "Solana": "SOL-USD",
-    "Dogecoin": "DOGE-USD",
-}
-
-# Determine the effective end date for each asset
-asset_end_dates = {}
-for asset in selected_assets:
-    if asset == "Fixed Deposit":
-        asset_end_dates[asset] = user_end_date
-    else:
-        if user_end_date > today:
-            asset_end_dates[asset] = today
-        else:
+    # Determine the effective end date for each asset
+    asset_end_dates = {}
+    for asset in selected_assets:
+        if asset == "Fixed Deposit":
             asset_end_dates[asset] = user_end_date
-
-# Warn the user if a future end date is selected for market assets
-if any(user_end_date > today and asset != "Fixed Deposit" for asset in selected_assets):
-    st.warning(f"Market data is only available up to today ({today}). For market assets, the end date has been set to today.")
-
-# Calculate returns for each selected asset
-asset_series = {}
-failed_assets = []
-actual_start_dates = {}
-
-for asset in selected_assets:
-    asset_start = start_date
-    asset_end = asset_end_dates[asset]
-    if asset == "Fixed Deposit":
-        fd_index = pd.date_range(start=asset_start, end=user_end_date)
-        daily_rate = (1 + fd_rate) ** (1/365) - 1
-        fd_values = initial_investment * (1 + daily_rate) ** np.arange(len(fd_index))
-        if currency == "SGD":
-            fd_values = fd_values * usd_to_sgd
-        asset_series[asset] = pd.Series(fd_values, index=fd_index)
-        actual_start_dates[asset] = asset_start
-    elif asset == "HDB":
-        hdb_values = calculate_hdb_returns(asset_start, asset_end, initial_investment)
-        if hdb_values is not None:
+        else:
+            if user_end_date > today:
+                asset_end_dates[asset] = today
+            else:
+                asset_end_dates[asset] = user_end_date
+
+    # Warn the user if a future end date is selected for market assets
+    if any(user_end_date > today and asset != "Fixed Deposit" for asset in selected_assets):
+        st.warning(f"Market data is only available up to today ({today}). For market assets, the end date has been set to today.")
+
+    # Calculate returns for each selected asset
+    asset_series = {}
+    failed_assets = []
+    actual_start_dates = {}
+
+    for asset in selected_assets:
+        asset_start = start_date
+        asset_end = asset_end_dates[asset]
+        if asset == "Fixed Deposit":
+            fd_index = pd.date_range(start=asset_start, end=user_end_date)
+            daily_rate = (1 + fd_rate) ** (1/365) - 1
+            fd_values = initial_investment * (1 + daily_rate) ** np.arange(len(fd_index))
             if currency == "SGD":
-                hdb_values = hdb_values * usd_to_sgd
-            asset_series[asset] = hdb_values
+                fd_values = fd_values * usd_to_sgd
+            asset_series[asset] = pd.Series(fd_values, index=fd_index)
             actual_start_dates[asset] = asset_start
+        elif asset == "HDB":
+            hdb_values = calculate_hdb_returns(asset_start, asset_end, initial_investment)
+            if hdb_values is not None:
+                if currency == "SGD":
+                    hdb_values = hdb_values * usd_to_sgd
+                asset_series[asset] = hdb_values
+                actual_start_dates[asset] = asset_start
+            else:
+                failed_assets.append(asset)
         else:
-            failed_assets.append(asset)
+            price_data = fetch_stock_daily(tickers[asset], asset_start, asset_end)
+            if price_data is not None and not price_data.empty:
+                price_data = price_data.sort_index()
+                actual_start = price_data.index[0]
+                actual_start_dates[asset] = actual_start
+                aligned_index = pd.date_range(start=actual_start, end=asset_end)
+                price_data = price_data.reindex(aligned_index)
+                price_data = price_data.ffill()
+                asset_values = initial_investment * (price_data / price_data.iloc[0])
+                if currency == "SGD":
+                    asset_values = asset_values * usd_to_sgd
+                asset_series[asset] = asset_values
+            else:
+                failed_assets.append(asset)
+
+    # Combine all asset series into a single DataFrame
+    if asset_series:
+        returns_data = pd.DataFrame(asset_series)
     else:
-        price_data = fetch_yfinance_daily(tickers[asset], asset_start, asset_end)
-        if price_data is not None and not price_data.empty:
-            price_data = price_data.sort_index()
-            actual_start = price_data.index[0]
-            actual_start_dates[asset] = actual_start
-            aligned_index = pd.date_range(start=actual_start, end=asset_end)
-            price_data = price_data.reindex(aligned_index)
-            price_data = price_data.ffill()
-            asset_values = initial_investment * (price_data / price_data.iloc[0])
-            if currency == "SGD":
-                asset_values = asset_values * usd_to_sgd
-            asset_series[asset] = asset_values
+        returns_data = pd.DataFrame()
+
+    # Remove failed assets from selected_assets (except FD)
+    selected_assets = [asset for asset in selected_assets if asset not in failed_assets or asset == "Fixed Deposit"]
+
+    if not selected_assets:
+        st.error("No assets could be loaded. Please try different assets.")
+        st.stop()
+
+    # Create the plot
+    fig = go.Figure()
+
+    # Add vertical lines for every 5 years
+    start_year = returns_data.index[0].year
+    end_year = returns_data.index[-1].year
+    for year in range(start_year, end_year + 1, 5):
+        fig.add_vline(x=datetime(year, 1, 1), line_dash="dash", line_color="gray", opacity=0.3)
+
+    for asset in selected_assets:
+        fig.add_trace(go.Scatter(
+            x=returns_data.index,
+            y=returns_data[asset],
+            name=asset,
+            mode='lines'
+        ))
+
+    fig.update_layout(
+        title="Asset Performance Comparison",
+        xaxis_title="Date",
+        yaxis_title=f"Investment Value ({currency_symbol})",
+        hovermode="x unified",
+        height=600,
+        yaxis_type="log" if use_log_scale else "linear"
+    )
+
+    # Display the plot
+    st.plotly_chart(fig, use_container_width=True)
+
+    # Create a summary table
+    st.subheader("Investment Summary")
+    summary_data = []
+    for asset in selected_assets:
+        valid_series = returns_data[asset].dropna()
+        if not valid_series.empty:
+            final_value = valid_series.iloc[-1]
+            days = (valid_series.index[-1] - valid_series.index[0]).days
+            years = days / 365
+            annualized_return = ((final_value / initial_investment) ** (1/years) - 1) * 100
+            
+            # Calculate yearly return statistics
+            yearly_data = valid_series.resample('YE').first()
+            yearly_returns = yearly_data.pct_change().dropna()
+            positive_years = (yearly_returns > 0).sum()
+            total_years = len(yearly_returns)
+            positive_percentage = (positive_years / total_years) * 100
+            
+            # Calculate return multiple
+            return_multiple = final_value / initial_investment
+            
+            # Calculate simple interest based annual return
+            simple_annual_return = ((return_multiple - 1) / years) * 100
+            
+            summary_data.append({
+                "Asset": asset,
+                f"Final Value ({currency_symbol})": final_value,
+                "Return Multiple": return_multiple,
+                "Annualized Return (%)": annualized_return,
+                "Simple Annual Return (%)": simple_annual_return,
+                "Positive Years": f"{positive_years}/{total_years}",
+                "Positive Years %": positive_percentage,
+            })
+        else:
+            summary_data.append({
+                "Asset": asset,
+                f"Final Value ({currency_symbol})": None,
+                "Return Multiple": None,
+                "Annualized Return (%)": None,
+                "Simple Annual Return (%)": None,
+                "Positive Years": "N/A",
+                "Positive Years %": None,
+            })
+
+    # Convert to DataFrame
+    df = pd.DataFrame(summary_data)
+
+    # Format the display values
+    df[f"Final Value ({currency_symbol})"] = df[f"Final Value ({currency_symbol})"].apply(lambda x: f"{x:,.2f}" if x is not None else "N/A")
+    df["Return Multiple"] = df["Return Multiple"].apply(lambda x: f"{x:.2f}" if x is not None else "N/A")
+    df["Annualized Return (%)"] = df["Annualized Return (%)"].apply(lambda x: f"{x:.2f}" if x is not None else "N/A")
+    df["Simple Annual Return (%)"] = df["Simple Annual Return (%)"].apply(lambda x: f"{x:.2f}" if x is not None else "N/A")
+    df["Positive Years %"] = df["Positive Years %"].apply(lambda x: f"{x:.1f}" if x is not None else "N/A")
+
+    # Display the summary table with sorting enabled
+    st.dataframe(
+        df,
+        hide_index=True,
+        column_config={
+            f"Final Value ({currency_symbol})": st.column_config.NumberColumn(
+                format="%.2f"
+            ),
+            "Return Multiple": st.column_config.NumberColumn(
+                format="%.2f"
+            ),
+            "Annualized Return (%)": st.column_config.NumberColumn(
+                format="%.2f"
+            ),
+            "Simple Annual Return (%)": st.column_config.NumberColumn(
+                format="%.2f"
+            ),
+            "Positive Years %": st.column_config.NumberColumn(
+                format="%.1f"
+            ),
+            "Performance": st.column_config.ImageColumn(
+                "Performance"
+            )
+        }
+    )
+
+    # Calculate and display final returns
+    st.subheader("Final Investment Values")
+    for asset in selected_assets:
+        valid_series = returns_data[asset].dropna()
+        if not valid_series.empty:
+            final_value = valid_series.iloc[-1]
+            st.write(f"{asset}: {currency_symbol}{final_value:,.2f}")
         else:
-            failed_assets.append(asset)
+            st.write(f"{asset}: Data unavailable")
+
+    # Calculate and display annualized returns
+    st.subheader("Annualized Returns")
+    for asset in selected_assets:
+        valid_series = returns_data[asset].dropna()
+        if len(valid_series) > 1:
+            actual_start = actual_start_dates[asset]
+            days = (valid_series.index[-1] - valid_series.index[0]).days
+            years = days / 365
+            final_value = valid_series.iloc[-1]
+            annualized_return = ((final_value / initial_investment) ** (1/years) - 1) * 100
+            if pd.Timestamp(actual_start).date() > start_date:
+                st.write(f"{asset}: {annualized_return:.2f}% (Data available from {actual_start.strftime('%Y-%m-%d')})")
+            else:
+                st.write(f"{asset}: {annualized_return:.2f}%")
+        else:
+            st.write(f"{asset}: N/A")
+
+    # Calculate and display yearly return statistics
+    st.subheader("Yearly Return Statistics")
+    for asset in selected_assets:
+        valid_series = returns_data[asset].dropna()
+        if len(valid_series) > 1:
+            # Resample to yearly data
+            yearly_data = valid_series.resample('YE').first()
+            
+            # Calculate yearly returns
+            yearly_returns = yearly_data.pct_change().dropna()
+            
+            # Count positive and negative years
+            positive_years = (yearly_returns > 0).sum()
+            total_years = len(yearly_returns)
+            positive_percentage = (positive_years / total_years) * 100
+            
+            st.write(f"{asset}: {positive_years} out of {total_years} years ({positive_percentage:.1f}%) had positive returns")
+        else:
+            st.write(f"{asset}: Insufficient data for yearly analysis")
 
-# Combine all asset series into a single DataFrame
-if asset_series:
-    returns_data = pd.DataFrame(asset_series)
-else:
-    returns_data = pd.DataFrame()
-
-# Remove failed assets from selected_assets (except FD)
-selected_assets = [asset for asset in selected_assets if asset not in failed_assets or asset == "Fixed Deposit"]
-
-if not selected_assets:
-    st.error("No assets could be loaded. Please try different assets.")
-    st.stop()
-
-# Create the plot
-fig = go.Figure()
-
-# Add vertical lines for every 5 years
-start_year = returns_data.index[0].year
-end_year = returns_data.index[-1].year
-for year in range(start_year, end_year + 1, 5):
-    fig.add_vline(x=datetime(year, 1, 1), line_dash="dash", line_color="gray", opacity=0.3)
-
-for asset in selected_assets:
-    fig.add_trace(go.Scatter(
-        x=returns_data.index,
-        y=returns_data[asset],
-        name=asset,
-        mode='lines'
-    ))
-
-fig.update_layout(
-    title="Asset Performance Comparison",
-    xaxis_title="Date",
-    yaxis_title=f"Investment Value ({currency_symbol})",
-    hovermode="x unified",
-    height=600,
-    yaxis_type="log" if use_log_scale else "linear"
-)
+    # Show warnings for data availability
+    for asset in selected_assets:
+        if asset in actual_start_dates and pd.Timestamp(actual_start_dates[asset]).date() > start_date:
+            st.warning(f"Data for {asset} is only available from {actual_start_dates[asset].strftime('%Y-%m-%d')}. The analysis starts from this date.")
+
+    # Show warning for failed assets
+    if failed_assets:
+        st.warning(f"Could not load data for the following assets: {', '.join(failed_assets)}")
 
-# Display the plot
-st.plotly_chart(fig, use_container_width=True)
-
-# Create a summary table
-st.subheader("Investment Summary")
-summary_data = []
-for asset in selected_assets:
-    valid_series = returns_data[asset].dropna()
-    if not valid_series.empty:
-        final_value = valid_series.iloc[-1]
-        days = (valid_series.index[-1] - valid_series.index[0]).days
-        years = days / 365
-        annualized_return = ((final_value / initial_investment) ** (1/years) - 1) * 100
+else:
+    # README & FORMULAS TAB
+    st.header("Investment Calculation Guide")
+    
+    st.markdown("""
+    ## Key Investment Formulas
+    
+    This guide explains the key formulas used in the investment comparison tool and how to interpret them.
+    
+    ### Return Multiple
+    
+    The return multiple shows how many times your initial investment has grown over the investment horizon.
+    
+    ```
+    Return Multiple = Final Value / Initial Investment
+    ```
+    
+    **Example:**
+    - Initial Investment: $10,000
+    - Final Value: $25,000
+    - Return Multiple = 25,000 / 10,000 = 2.5x
+    
+    This means your investment has grown to 2.5 times its original value over the investment period.
+    
+    ### Annualized Return (CAGR)
+    
+    The annualized return is the geometric average annual return over the investment period, expressed as a percentage. This is equivalent to the Compound Annual Growth Rate (CAGR).
+    
+    ```
+    Annualized Return (%) = ((Final Value / Initial Investment) ^ (1 / Years) - 1) × 100
+    ```
+    
+    **Example:**
+    - Initial Investment: $10,000
+    - Final Value: $20,000
+    - Investment Period: 10 years
+    - Annualized Return = ((20,000 / 10,000) ^ (1/10) - 1) × 100 = 7.18%
+    
+    This means your investment grew at an average rate of 7.18% per year, compounded annually.
+    
+    ### Simple Annual Return
+    
+    Simple annual return calculates the average yearly return by dividing the total return by the number of years.
+    
+    ```
+    Simple Annual Return (%) = ((Return Multiple - 1) / Years) × 100
+    ```
+    
+    **Example:**
+    - Return Multiple: 2.0x
+    - Investment Period: 10 years
+    - Simple Annual Return = ((2.0 - 1) / 10) × 100 = 10.0%
+    
+    This represents the linear average annual return, which is typically higher than CAGR.
+    
+    ### CAGR vs. Simple Annual Return
+    
+    **Compound Annual Growth Rate (CAGR)** and simple annual return represent two different ways to measure annual performance:
+    
+    - **CAGR** (our "Annualized Return") assumes compounding - each year's gains build upon the previous year's value
+    - **Simple Annual Return** assumes linear growth - dividing the total gain by the number of years
+    
+    For the same investment period and final value, the simple annual return is typically higher than CAGR. The difference increases with longer investment periods and higher returns.
+    
+    **Mathematical relationship:**
+    - CAGR: (1 + r)^n = Final Value / Initial Value
+    - Simple: 1 + (r × n) = Final Value / Initial Value
+    
+    Where r is the rate (as a decimal) and n is the number of years.
+    
+    ### Calculating Final Value from Annualized Return
+    
+    If you know the annualized return, you can calculate the expected final value:
+    
+    ```
+    Final Value = Initial Investment × (1 + Annualized Return/100) ^ Years
+    ```
+    
+    **Example:**
+    - Initial Investment: $10,000
+    - Annualized Return: 7%
+    - Investment Period: 10 years
+    - Final Value = $10000 × (1 + 0.07)^{10} = 19672$
+    
+    ### Positive Years Percentage
+    
+    The percentage of years with positive returns:
+    
+    ```
+    Positive Years (%) = (Number of Years with Positive Returns / Total Years) × 100
+    ```
+    
+    This helps assess the consistency of positive performance.
+    """)
+    
+    st.subheader("How to Use This Information")
+    
+    st.markdown("""
+    - **Return Multiple** is useful for quickly understanding total growth
+    - **Annualized Return** allows fair comparison between investments with different time periods
+    - **Positive Years %** helps assess the consistency and volatility of returns
+    
+    When comparing investments:
+    1. Higher annualized returns are generally better
+    2. More consistent returns (higher positive years %) may indicate lower volatility
+    3. Return multiples must be viewed in context of the time period
+    """)
+    
+    # Add interactive investment calculator
+    st.subheader("Investment Calculator")
+    
+    st.markdown("""
+    Use this calculator to estimate future investment values based on different parameters.
+    """)
+    
+    # Add explanation about simple vs compound interest
+    with st.expander("Simple vs. Compound Interest"):
+        st.markdown("""
+        ### Simple Interest vs. Compound Interest
         
-        # Calculate yearly return statistics
-        yearly_data = valid_series.resample('YE').first()
-        yearly_returns = yearly_data.pct_change().dropna()
-        positive_years = (yearly_returns > 0).sum()
-        total_years = len(yearly_returns)
-        positive_percentage = (positive_years / total_years) * 100
+        **Simple Interest** is calculated only on the initial principal, without considering accumulated interest.
         
-        summary_data.append({
-            "Asset": asset,
-            f"Final Value ({currency_symbol})": final_value,
-            "Annualized Return (%)": annualized_return,
-            "Positive Years": f"{positive_years}/{total_years}",
-            "Positive Years %": positive_percentage,
-        })
-    else:
-        summary_data.append({
-            "Asset": asset,
-            f"Final Value ({currency_symbol})": None,
-            "Annualized Return (%)": None,
-            "Positive Years": "N/A",
-            "Positive Years %": None,
-        })
-
-# Convert to DataFrame
-df = pd.DataFrame(summary_data)
-
-# Format the display values
-df[f"Final Value ({currency_symbol})"] = df[f"Final Value ({currency_symbol})"].apply(lambda x: f"{x:,.2f}" if x is not None else "N/A")
-df["Annualized Return (%)"] = df["Annualized Return (%)"].apply(lambda x: f"{x:.2f}" if x is not None else "N/A")
-df["Positive Years %"] = df["Positive Years %"].apply(lambda x: f"{x:.1f}" if x is not None else "N/A")
-
-# Display the summary table with sorting enabled
-st.dataframe(
-    df,
-    hide_index=True,
-    column_config={
-        f"Final Value ({currency_symbol})": st.column_config.NumberColumn(
-            format="%.2f"
-        ),
-        "Annualized Return (%)": st.column_config.NumberColumn(
-            format="%.2f"
-        ),
-        "Positive Years %": st.column_config.NumberColumn(
-            format="%.1f"
-        ),
-        "Performance": st.column_config.ImageColumn(
-            "Performance"
-        )
-    }
-)
-
-# Calculate and display final returns
-st.subheader("Final Investment Values")
-for asset in selected_assets:
-    valid_series = returns_data[asset].dropna()
-    if not valid_series.empty:
-        final_value = valid_series.iloc[-1]
-        st.write(f"{asset}: {currency_symbol}{final_value:,.2f}")
-    else:
-        st.write(f"{asset}: Data unavailable")
-
-# Calculate and display annualized returns
-st.subheader("Annualized Returns")
-for asset in selected_assets:
-    valid_series = returns_data[asset].dropna()
-    if len(valid_series) > 1:
-        actual_start = actual_start_dates[asset]
-        days = (valid_series.index[-1] - valid_series.index[0]).days
-        years = days / 365
-        final_value = valid_series.iloc[-1]
-        annualized_return = ((final_value / initial_investment) ** (1/years) - 1) * 100
-        if pd.Timestamp(actual_start).date() > start_date:
-            st.write(f"{asset}: {annualized_return:.2f}% (Data available from {actual_start.strftime('%Y-%m-%d')})")
-        else:
-            st.write(f"{asset}: {annualized_return:.2f}%")
-    else:
-        st.write(f"{asset}: N/A")
-
-# Calculate and display yearly return statistics
-st.subheader("Yearly Return Statistics")
-for asset in selected_assets:
-    valid_series = returns_data[asset].dropna()
-    if len(valid_series) > 1:
-        # Resample to yearly data
-        yearly_data = valid_series.resample('YE').first()
+        ```
+        Final Value = Initial Investment × (1 + (Annual Return/100 × Years))
+        ```
+        
+        **Compound Interest** is calculated on both the initial principal and the accumulated interest.
         
-        # Calculate yearly returns
-        yearly_returns = yearly_data.pct_change().dropna()
+        ```
+        Final Value = Initial Investment × (1 + Annual Return/100)^Years
+        ```
         
-        # Count positive and negative years
-        positive_years = (yearly_returns > 0).sum()
-        total_years = len(yearly_returns)
-        positive_percentage = (positive_years / total_years) * 100
+        **Key Differences:**
+        - Simple interest grows linearly (straight line on chart)
+        - Compound interest grows exponentially (curved line on chart)
+        - Over long periods, the difference becomes substantial
+        - Most real-world investments compound (stocks, bonds, etc.)
+        """)
+    
+    calc_col1, calc_col2 = st.columns(2)
+    
+    with calc_col1:
+        calc_initial = st.number_input("Initial Investment ($)", min_value=100, value=10000, step=1000)
+        calc_years = st.number_input("Investment Period (Years)", min_value=1, value=10, step=1)
+        interest_type = st.radio("Interest Type", ["Compound", "Simple"], index=0)
+    
+    with calc_col2:
+        calc_return = st.number_input("Annual Return (%)", min_value=0.0, value=7.0, step=0.5)
+        calc_currency = st.selectbox("Currency", ["USD", "SGD"], index=0)
+    
+    # Calculate results based on interest type
+    calc_currency_symbol = "$" if calc_currency == "USD" else "S$"
+    
+    if interest_type == "Compound":
+        calc_final_value = calc_initial * ((1 + calc_return/100) ** calc_years)
+        interest_formula = f"${calc_initial:,.0f} × (1 + {calc_return/100:.4f})^{calc_years}$"
+    else:  # Simple Interest
+        calc_final_value = calc_initial * (1 + (calc_return/100 * calc_years))
+        interest_formula = f"${calc_initial:,.0f} × (1 + {calc_return/100:.4f} × {calc_years})$"
+    
+    calc_return_multiple = calc_final_value / calc_initial
+    total_interest = calc_final_value - calc_initial
+    
+    # Display results in a highlighted box
+    st.markdown(f"""
+    #### Results
+    """)
+    
+    results_col1, results_col2, results_col3 = st.columns(3)
+    
+    with results_col1:
+        st.metric("Final Investment Value", f"{calc_currency_symbol}{calc_final_value:,.2f}")
         
-        st.write(f"{asset}: {positive_years} out of {total_years} years ({positive_percentage:.1f}%) had positive returns")
+    with results_col2:
+        st.metric("Return Multiple", f"{calc_return_multiple:.2f}x")
+        
+    with results_col3:
+        st.metric("Total Interest Earned", f"{calc_currency_symbol}{total_interest:,.2f}")
+    
+    st.markdown(f"**Formula Applied:** {interest_formula} = {calc_currency_symbol}{calc_final_value:,.2f}")
+    
+    # Add a chart to visualize growth and compare both methods
+    years_range = list(range(0, calc_years + 1))
+    
+    # Calculate both types of growth for comparison
+    compound_values = [calc_initial * ((1 + calc_return/100) ** year) for year in years_range]
+    simple_values = [calc_initial * (1 + (calc_return/100 * year)) for year in years_range]
+    
+    # Create DataFrame with both growth types
+    growth_chart = pd.DataFrame({
+        'Year': years_range,
+        'Compound Interest': compound_values,
+        'Simple Interest': simple_values
+    })
+    
+    st.subheader("Investment Growth Projection")
+    
+    # Highlight the selected interest type
+    if interest_type == "Compound":
+        st.line_chart(growth_chart, x='Year', y=['Compound Interest', 'Simple Interest'])
     else:
-        st.write(f"{asset}: Insufficient data for yearly analysis")
-
-# Show warnings for data availability
-for asset in selected_assets:
-    if asset in actual_start_dates and pd.Timestamp(actual_start_dates[asset]).date() > start_date:
-        st.warning(f"Data for {asset} is only available from {actual_start_dates[asset].strftime('%Y-%m-%d')}. The analysis starts from this date.")
-
-# Show warning for failed assets
-if failed_assets:
-    st.warning(f"Could not load data for the following assets: {', '.join(failed_assets)}")
+        st.line_chart(growth_chart, x='Year', y=['Simple Interest', 'Compound Interest'])

app_archived_v1.py

@@ -0,0 +1,329 @@
+import streamlit as st
+import pandas as pd
+import yfinance as yf
+import plotly.graph_objects as go
+from datetime import datetime, timedelta
+import numpy as np
+
+# Import utility functions
+from utils.yfinance_utils import fetch_yfinance_daily
+from utils.currency_utils import get_usd_sgd_rate
+from utils.fd_utils import calculate_fd_returns
+from utils.hdb_utils import calculate_hdb_returns
+
+
+print("Starting the app ...")
+
+# Sanity check on the yfinance_utils
+print("Sanity check on the yfinance_utils ...")
+# print(fetch_yfinance_daily("MSFT", "2020-01-01", "2020-01-03"))
+data = yf.download("MSFT", "2020-01-01", "2020-01-03")
+print(data.head())
+
+# Set page config
+st.set_page_config(page_title="Asset Class Comparison", layout="wide")
+
+# Title and description
+st.title("Asset Class Performance Comparison")
+st.write("Compare the performance of different asset classes over time")
+# st.write("Note: Cryptocurrencies (BTC, ETH, SOL, DOGE) are highly volatile and should be considered high-risk investments")
+
+# Sidebar for user inputs
+st.sidebar.header("Investment Parameters")
+currency = st.sidebar.selectbox("Display Currency", ["USD", "SGD"], index=0)
+initial_investment = st.sidebar.number_input(f"Initial Investment Amount ({currency})", min_value=1000, value=10000, step=1000)
+start_date = st.sidebar.date_input("Start Date", value=datetime.now() - timedelta(days=365*25))
+user_end_date = st.sidebar.date_input("End Date", value=datetime.now())
+
+fd_rate = st.sidebar.number_input("Fixed Deposit Rate (%)", min_value=0.0, value=2.9, step=0.1) / 100
+use_log_scale = st.sidebar.checkbox("Use Log Scale", value=True)
+
+# Calculate and display investment period
+investment_days = (user_end_date - start_date).days
+investment_years = investment_days / 365
+st.write(f"Investment Period: {investment_days} days ({investment_years:.1f} years)")
+
+# Asset selection
+selected_assets = st.sidebar.multiselect(
+    "Select Assets to Compare",
+    [
+        "Fixed Deposit",
+        "HDB",
+        "Gold",
+        "SGS Bonds",
+        "US Treasury Bonds",
+        "NASDAQ Composite",
+        "NASDAQ Large Cap",
+        "NASDAQ 100",
+        "S&P 500",
+        "Dow Jones",
+        "Microsoft",
+        "Google",
+        "Nvidia",
+        "Apple",
+        "Amazon",
+        "Tesla",
+        "Netflix",
+        "Meta",
+        "Bitcoin",
+        "Ethereum",
+        "Solana",
+        "Dogecoin",
+    ],
+    default=[
+        "Fixed Deposit", 
+        "HDB",
+        "Gold", 
+        "US Treasury Bonds", "SGS Bonds", 
+        "S&P 500", "Dow Jones", "NASDAQ Composite", #"NASDAQ Large Cap", "NASDAQ 100",
+        "Microsoft", "Google", "Nvidia",
+        "Bitcoin"  
+        ]
+)
+
+# Today's date for reference
+today = datetime.now().date()
+
+usd_to_sgd = get_usd_sgd_rate() if currency == "SGD" else 1.0
+currency_symbol = "$" if currency == "USD" else "S$"
+
+# Create a dictionary of tickers for yfinance
+tickers = {
+    "Gold": "GC=F",
+    "HDB": "A12.SI",
+    "SGS Bonds": "A35.SI",  # Nikko AM SGD Investment Grade Corporate Bond ETF
+    "US Treasury Bonds": "TLT",  # iShares 20+ Year Treasury Bond ETF
+    "NASDAQ Composite": "^IXIC",
+    "NASDAQ Large Cap": "^NDX",
+    "NASDAQ 100": "^NDX",
+    "S&P 500": "^GSPC",
+    "Dow Jones": "^DJI",
+    "Microsoft": "MSFT",
+    "Google": "GOOGL",
+    "Nvidia": "NVDA",
+    "Apple": "AAPL",
+    "Amazon": "AMZN",
+    "Tesla": "TSLA",
+    "Netflix": "NFLX",
+    "Meta": "META",
+    "Bitcoin": "BTC-USD",
+    "Ethereum": "ETH-USD",
+    "Solana": "SOL-USD",
+    "Dogecoin": "DOGE-USD",
+}
+
+# Determine the effective end date for each asset
+asset_end_dates = {}
+for asset in selected_assets:
+    if asset == "Fixed Deposit":
+        asset_end_dates[asset] = user_end_date
+    else:
+        if user_end_date > today:
+            asset_end_dates[asset] = today
+        else:
+            asset_end_dates[asset] = user_end_date
+
+# Warn the user if a future end date is selected for market assets
+if any(user_end_date > today and asset != "Fixed Deposit" for asset in selected_assets):
+    st.warning(f"Market data is only available up to today ({today}). For market assets, the end date has been set to today.")
+
+# Calculate returns for each selected asset
+asset_series = {}
+failed_assets = []
+actual_start_dates = {}
+
+for asset in selected_assets:
+    asset_start = start_date
+    asset_end = asset_end_dates[asset]
+    if asset == "Fixed Deposit":
+        fd_index = pd.date_range(start=asset_start, end=user_end_date)
+        daily_rate = (1 + fd_rate) ** (1/365) - 1
+        fd_values = initial_investment * (1 + daily_rate) ** np.arange(len(fd_index))
+        if currency == "SGD":
+            fd_values = fd_values * usd_to_sgd
+        asset_series[asset] = pd.Series(fd_values, index=fd_index)
+        actual_start_dates[asset] = asset_start
+    elif asset == "HDB":
+        hdb_values = calculate_hdb_returns(asset_start, asset_end, initial_investment)
+        if hdb_values is not None:
+            if currency == "SGD":
+                hdb_values = hdb_values * usd_to_sgd
+            asset_series[asset] = hdb_values
+            actual_start_dates[asset] = asset_start
+        else:
+            failed_assets.append(asset)
+    else:
+        price_data = fetch_yfinance_daily(tickers[asset], asset_start, asset_end)
+        if price_data is not None and not price_data.empty:
+            price_data = price_data.sort_index()
+            actual_start = price_data.index[0]
+            actual_start_dates[asset] = actual_start
+            aligned_index = pd.date_range(start=actual_start, end=asset_end)
+            price_data = price_data.reindex(aligned_index)
+            price_data = price_data.ffill()
+            asset_values = initial_investment * (price_data / price_data.iloc[0])
+            if currency == "SGD":
+                asset_values = asset_values * usd_to_sgd
+            asset_series[asset] = asset_values
+        else:
+            failed_assets.append(asset)
+
+# Combine all asset series into a single DataFrame
+if asset_series:
+    returns_data = pd.DataFrame(asset_series)
+else:
+    returns_data = pd.DataFrame()
+
+# Remove failed assets from selected_assets (except FD)
+selected_assets = [asset for asset in selected_assets if asset not in failed_assets or asset == "Fixed Deposit"]
+
+if not selected_assets:
+    st.error("No assets could be loaded. Please try different assets.")
+    st.stop()
+
+# Create the plot
+fig = go.Figure()
+
+# Add vertical lines for every 5 years
+start_year = returns_data.index[0].year
+end_year = returns_data.index[-1].year
+for year in range(start_year, end_year + 1, 5):
+    fig.add_vline(x=datetime(year, 1, 1), line_dash="dash", line_color="gray", opacity=0.3)
+
+for asset in selected_assets:
+    fig.add_trace(go.Scatter(
+        x=returns_data.index,
+        y=returns_data[asset],
+        name=asset,
+        mode='lines'
+    ))
+
+fig.update_layout(
+    title="Asset Performance Comparison",
+    xaxis_title="Date",
+    yaxis_title=f"Investment Value ({currency_symbol})",
+    hovermode="x unified",
+    height=600,
+    yaxis_type="log" if use_log_scale else "linear"
+)
+
+# Display the plot
+st.plotly_chart(fig, use_container_width=True)
+
+# Create a summary table
+st.subheader("Investment Summary")
+summary_data = []
+for asset in selected_assets:
+    valid_series = returns_data[asset].dropna()
+    if not valid_series.empty:
+        final_value = valid_series.iloc[-1]
+        days = (valid_series.index[-1] - valid_series.index[0]).days
+        years = days / 365
+        annualized_return = ((final_value / initial_investment) ** (1/years) - 1) * 100
+        
+        # Calculate yearly return statistics
+        yearly_data = valid_series.resample('YE').first()
+        yearly_returns = yearly_data.pct_change().dropna()
+        positive_years = (yearly_returns > 0).sum()
+        total_years = len(yearly_returns)
+        positive_percentage = (positive_years / total_years) * 100
+        
+        summary_data.append({
+            "Asset": asset,
+            f"Final Value ({currency_symbol})": final_value,
+            "Annualized Return (%)": annualized_return,
+            "Positive Years": f"{positive_years}/{total_years}",
+            "Positive Years %": positive_percentage,
+        })
+    else:
+        summary_data.append({
+            "Asset": asset,
+            f"Final Value ({currency_symbol})": None,
+            "Annualized Return (%)": None,
+            "Positive Years": "N/A",
+            "Positive Years %": None,
+        })
+
+# Convert to DataFrame
+df = pd.DataFrame(summary_data)
+
+# Format the display values
+df[f"Final Value ({currency_symbol})"] = df[f"Final Value ({currency_symbol})"].apply(lambda x: f"{x:,.2f}" if x is not None else "N/A")
+df["Annualized Return (%)"] = df["Annualized Return (%)"].apply(lambda x: f"{x:.2f}" if x is not None else "N/A")
+df["Positive Years %"] = df["Positive Years %"].apply(lambda x: f"{x:.1f}" if x is not None else "N/A")
+
+# Display the summary table with sorting enabled
+st.dataframe(
+    df,
+    hide_index=True,
+    column_config={
+        f"Final Value ({currency_symbol})": st.column_config.NumberColumn(
+            format="%.2f"
+        ),
+        "Annualized Return (%)": st.column_config.NumberColumn(
+            format="%.2f"
+        ),
+        "Positive Years %": st.column_config.NumberColumn(
+            format="%.1f"
+        ),
+        "Performance": st.column_config.ImageColumn(
+            "Performance"
+        )
+    }
+)
+
+# Calculate and display final returns
+st.subheader("Final Investment Values")
+for asset in selected_assets:
+    valid_series = returns_data[asset].dropna()
+    if not valid_series.empty:
+        final_value = valid_series.iloc[-1]
+        st.write(f"{asset}: {currency_symbol}{final_value:,.2f}")
+    else:
+        st.write(f"{asset}: Data unavailable")
+
+# Calculate and display annualized returns
+st.subheader("Annualized Returns")
+for asset in selected_assets:
+    valid_series = returns_data[asset].dropna()
+    if len(valid_series) > 1:
+        actual_start = actual_start_dates[asset]
+        days = (valid_series.index[-1] - valid_series.index[0]).days
+        years = days / 365
+        final_value = valid_series.iloc[-1]
+        annualized_return = ((final_value / initial_investment) ** (1/years) - 1) * 100
+        if pd.Timestamp(actual_start).date() > start_date:
+            st.write(f"{asset}: {annualized_return:.2f}% (Data available from {actual_start.strftime('%Y-%m-%d')})")
+        else:
+            st.write(f"{asset}: {annualized_return:.2f}%")
+    else:
+        st.write(f"{asset}: N/A")
+
+# Calculate and display yearly return statistics
+st.subheader("Yearly Return Statistics")
+for asset in selected_assets:
+    valid_series = returns_data[asset].dropna()
+    if len(valid_series) > 1:
+        # Resample to yearly data
+        yearly_data = valid_series.resample('YE').first()
+        
+        # Calculate yearly returns
+        yearly_returns = yearly_data.pct_change().dropna()
+        
+        # Count positive and negative years
+        positive_years = (yearly_returns > 0).sum()
+        total_years = len(yearly_returns)
+        positive_percentage = (positive_years / total_years) * 100
+        
+        st.write(f"{asset}: {positive_years} out of {total_years} years ({positive_percentage:.1f}%) had positive returns")
+    else:
+        st.write(f"{asset}: Insufficient data for yearly analysis")
+
+# Show warnings for data availability
+for asset in selected_assets:
+    if asset in actual_start_dates and pd.Timestamp(actual_start_dates[asset]).date() > start_date:
+        st.warning(f"Data for {asset} is only available from {actual_start_dates[asset].strftime('%Y-%m-%d')}. The analysis starts from this date.")
+
+# Show warning for failed assets
+if failed_assets:
+    st.warning(f"Could not load data for the following assets: {', '.join(failed_assets)}")

notebooks/download-stock-tickers.ipynb

@@ -0,0 +1,1025 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "de34e023",
+   "metadata": {},
+   "source": [
+    "# Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fd7ebd88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import FinanceDataReader as fdr\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d200466",
+   "metadata": {},
+   "source": [
+    "# Asset metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d18b01df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Symbol</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Description</th>\n",
+       "      <th>Asset Class</th>\n",
+       "      <th>FinanceDataReader</th>\n",
+       "      <th>stooq.com</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>^IXIC</td>\n",
+       "      <td>NASDAQ Composite</td>\n",
+       "      <td>Index</td>\n",
+       "      <td>Tracks ~3,000 stocks listed on NASDAQ</td>\n",
+       "      <td>Equity Index</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>^NDX</td>\n",
+       "      <td>NASDAQ-100</td>\n",
+       "      <td>Index</td>\n",
+       "      <td>Tracks top 100 non-financial companies on NASDAQ</td>\n",
+       "      <td>Equity Index</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>QQQ</td>\n",
+       "      <td>Invesco QQQ Trust</td>\n",
+       "      <td>ETF</td>\n",
+       "      <td>ETF that tracks the NASDAQ-100 index</td>\n",
+       "      <td>Equity ETF</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ONEQ</td>\n",
+       "      <td>Fidelity NASDAQ Composite Index ETF</td>\n",
+       "      <td>ETF</td>\n",
+       "      <td>ETF that tracks the NASDAQ Composite index</td>\n",
+       "      <td>Equity ETF</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>QQQM</td>\n",
+       "      <td>Invesco NASDAQ 100 ETF</td>\n",
+       "      <td>ETF</td>\n",
+       "      <td>Lower-cost version of QQQ, also tracks NASDAQ-100</td>\n",
+       "      <td>Equity ETF</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>A35.SI</td>\n",
+       "      <td>SGS Bonds</td>\n",
+       "      <td>ETF</td>\n",
+       "      <td>Nikko AM SGD Investment Grade Corporate Bond E...</td>\n",
+       "      <td>Bond (Singapore)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>TLT</td>\n",
+       "      <td>US Treasury Bonds</td>\n",
+       "      <td>ETF</td>\n",
+       "      <td>iShares 20+ Year Treasury Bond ETF</td>\n",
+       "      <td>Bond (US)</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>^GSPC</td>\n",
+       "      <td>S&amp;P 500</td>\n",
+       "      <td>Index</td>\n",
+       "      <td>Tracks 500 largest U.S. companies</td>\n",
+       "      <td>Equity Index</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>^DJI</td>\n",
+       "      <td>Dow Jones</td>\n",
+       "      <td>Index</td>\n",
+       "      <td>Tracks 30 large U.S. companies, price-weighted</td>\n",
+       "      <td>Equity Index</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>MSFT</td>\n",
+       "      <td>Microsoft</td>\n",
+       "      <td>Stock</td>\n",
+       "      <td>U.S. tech giant, member of all major U.S. indexes</td>\n",
+       "      <td>Equity</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>GOOGL</td>\n",
+       "      <td>Google (Alphabet)</td>\n",
+       "      <td>Stock</td>\n",
+       "      <td>Class A shares of Alphabet Inc., parent compan...</td>\n",
+       "      <td>Equity</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>NVDA</td>\n",
+       "      <td>Nvidia</td>\n",
+       "      <td>Stock</td>\n",
+       "      <td>Leading GPU and AI chipmaker</td>\n",
+       "      <td>Equity</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>Apple</td>\n",
+       "      <td>Stock</td>\n",
+       "      <td>Largest U.S. company by market cap</td>\n",
+       "      <td>Equity</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>AMZN</td>\n",
+       "      <td>Amazon</td>\n",
+       "      <td>Stock</td>\n",
+       "      <td>E-commerce and cloud computing giant</td>\n",
+       "      <td>Equity</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>Tesla</td>\n",
+       "      <td>Stock</td>\n",
+       "      <td>Electric vehicle and energy company</td>\n",
+       "      <td>Equity</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>NFLX</td>\n",
+       "      <td>Netflix</td>\n",
+       "      <td>Stock</td>\n",
+       "      <td>Streaming and media company</td>\n",
+       "      <td>Equity</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>META</td>\n",
+       "      <td>Meta (Facebook)</td>\n",
+       "      <td>Stock</td>\n",
+       "      <td>Parent company of Facebook, Instagram, WhatsApp</td>\n",
+       "      <td>Equity</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>BTC-USD</td>\n",
+       "      <td>Bitcoin</td>\n",
+       "      <td>Crypto</td>\n",
+       "      <td>Decentralized digital currency</td>\n",
+       "      <td>Cryptocurrency</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>ETH-USD</td>\n",
+       "      <td>Ethereum</td>\n",
+       "      <td>Crypto</td>\n",
+       "      <td>Smart contract and decentralized app platform</td>\n",
+       "      <td>Cryptocurrency</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>SOL-USD</td>\n",
+       "      <td>Solana</td>\n",
+       "      <td>Crypto</td>\n",
+       "      <td>High-performance blockchain with low transacti...</td>\n",
+       "      <td>Cryptocurrency</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>DOGE-USD</td>\n",
+       "      <td>Dogecoin</td>\n",
+       "      <td>Crypto</td>\n",
+       "      <td>Meme-inspired coin that gained traction</td>\n",
+       "      <td>Cryptocurrency</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>GLD</td>\n",
+       "      <td>SPDR Gold Shares</td>\n",
+       "      <td>ETF</td>\n",
+       "      <td>Tracks the price of gold bullion, one of the l...</td>\n",
+       "      <td>Commodity ETF</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>IAU</td>\n",
+       "      <td>iShares Gold Trust</td>\n",
+       "      <td>ETF</td>\n",
+       "      <td>Low-cost ETF that tracks the price of gold, si...</td>\n",
+       "      <td>Commodity ETF</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>SGOL</td>\n",
+       "      <td>Aberdeen Standard Physical Gold Shares ETF</td>\n",
+       "      <td>ETF</td>\n",
+       "      <td>Physically-backed gold ETF with gold stored in...</td>\n",
+       "      <td>Commodity ETF</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>PHYS</td>\n",
+       "      <td>Sprott Physical Gold Trust</td>\n",
+       "      <td>Trust</td>\n",
+       "      <td>Closed-end trust that invests in physical gold...</td>\n",
+       "      <td>Commodity Trust</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Symbol                                        Name    Type  \\\n",
+       "0      ^IXIC                            NASDAQ Composite   Index   \n",
+       "1       ^NDX                                  NASDAQ-100   Index   \n",
+       "2        QQQ                           Invesco QQQ Trust     ETF   \n",
+       "3       ONEQ         Fidelity NASDAQ Composite Index ETF     ETF   \n",
+       "4       QQQM                      Invesco NASDAQ 100 ETF     ETF   \n",
+       "5     A35.SI                                   SGS Bonds     ETF   \n",
+       "6        TLT                           US Treasury Bonds     ETF   \n",
+       "7      ^GSPC                                     S&P 500   Index   \n",
+       "8       ^DJI                                   Dow Jones   Index   \n",
+       "9       MSFT                                   Microsoft   Stock   \n",
+       "10     GOOGL                           Google (Alphabet)   Stock   \n",
+       "11      NVDA                                      Nvidia   Stock   \n",
+       "12      AAPL                                       Apple   Stock   \n",
+       "13      AMZN                                      Amazon   Stock   \n",
+       "14      TSLA                                       Tesla   Stock   \n",
+       "15      NFLX                                     Netflix   Stock   \n",
+       "16      META                             Meta (Facebook)   Stock   \n",
+       "17   BTC-USD                                     Bitcoin  Crypto   \n",
+       "18   ETH-USD                                    Ethereum  Crypto   \n",
+       "19   SOL-USD                                      Solana  Crypto   \n",
+       "20  DOGE-USD                                    Dogecoin  Crypto   \n",
+       "21       GLD                            SPDR Gold Shares     ETF   \n",
+       "22       IAU                          iShares Gold Trust     ETF   \n",
+       "23      SGOL  Aberdeen Standard Physical Gold Shares ETF     ETF   \n",
+       "24      PHYS                  Sprott Physical Gold Trust   Trust   \n",
+       "\n",
+       "                                          Description       Asset Class  \\\n",
+       "0               Tracks ~3,000 stocks listed on NASDAQ      Equity Index   \n",
+       "1    Tracks top 100 non-financial companies on NASDAQ      Equity Index   \n",
+       "2                ETF that tracks the NASDAQ-100 index        Equity ETF   \n",
+       "3          ETF that tracks the NASDAQ Composite index        Equity ETF   \n",
+       "4   Lower-cost version of QQQ, also tracks NASDAQ-100        Equity ETF   \n",
+       "5   Nikko AM SGD Investment Grade Corporate Bond E...  Bond (Singapore)   \n",
+       "6                  iShares 20+ Year Treasury Bond ETF         Bond (US)   \n",
+       "7                   Tracks 500 largest U.S. companies      Equity Index   \n",
+       "8      Tracks 30 large U.S. companies, price-weighted      Equity Index   \n",
+       "9   U.S. tech giant, member of all major U.S. indexes            Equity   \n",
+       "10  Class A shares of Alphabet Inc., parent compan...            Equity   \n",
+       "11                       Leading GPU and AI chipmaker            Equity   \n",
+       "12                 Largest U.S. company by market cap            Equity   \n",
+       "13               E-commerce and cloud computing giant            Equity   \n",
+       "14                Electric vehicle and energy company            Equity   \n",
+       "15                        Streaming and media company            Equity   \n",
+       "16    Parent company of Facebook, Instagram, WhatsApp            Equity   \n",
+       "17                     Decentralized digital currency    Cryptocurrency   \n",
+       "18      Smart contract and decentralized app platform    Cryptocurrency   \n",
+       "19  High-performance blockchain with low transacti...    Cryptocurrency   \n",
+       "20            Meme-inspired coin that gained traction    Cryptocurrency   \n",
+       "21  Tracks the price of gold bullion, one of the l...     Commodity ETF   \n",
+       "22  Low-cost ETF that tracks the price of gold, si...     Commodity ETF   \n",
+       "23  Physically-backed gold ETF with gold stored in...     Commodity ETF   \n",
+       "24  Closed-end trust that invests in physical gold...   Commodity Trust   \n",
+       "\n",
+       "    FinanceDataReader  stooq.com  \n",
+       "0                True      False  \n",
+       "1               False       True  \n",
+       "2                True      False  \n",
+       "3                True      False  \n",
+       "4                True      False  \n",
+       "5                True      False  \n",
+       "6                True      False  \n",
+       "7                True      False  \n",
+       "8                True      False  \n",
+       "9                True      False  \n",
+       "10               True      False  \n",
+       "11               True      False  \n",
+       "12               True      False  \n",
+       "13               True      False  \n",
+       "14               True      False  \n",
+       "15               True      False  \n",
+       "16               True      False  \n",
+       "17               True      False  \n",
+       "18               True      False  \n",
+       "19               True      False  \n",
+       "20               True      False  \n",
+       "21               True      False  \n",
+       "22               True      False  \n",
+       "23               True      False  \n",
+       "24               True      False  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "assets = [\n",
+    "    [\n",
+    "        \"^IXIC\",\n",
+    "        \"NASDAQ Composite\",\n",
+    "        \"Index\",\n",
+    "        \"Tracks ~3,000 stocks listed on NASDAQ\",\n",
+    "        \"Equity Index\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"^NDX\",\n",
+    "        \"NASDAQ-100\",\n",
+    "        \"Index\",\n",
+    "        \"Tracks top 100 non-financial companies on NASDAQ\",\n",
+    "        \"Equity Index\",\n",
+    "        False,  # Only available from 2020-02-11\n",
+    "        True,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"QQQ\",\n",
+    "        \"Invesco QQQ Trust\",\n",
+    "        \"ETF\",\n",
+    "        \"ETF that tracks the NASDAQ-100 index\",\n",
+    "        \"Equity ETF\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"ONEQ\",\n",
+    "        \"Fidelity NASDAQ Composite Index ETF\",\n",
+    "        \"ETF\",\n",
+    "        \"ETF that tracks the NASDAQ Composite index\",\n",
+    "        \"Equity ETF\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"QQQM\",\n",
+    "        \"Invesco NASDAQ 100 ETF\",\n",
+    "        \"ETF\",\n",
+    "        \"Lower-cost version of QQQ, also tracks NASDAQ-100\",\n",
+    "        \"Equity ETF\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"A35.SI\",\n",
+    "        \"SGS Bonds\",\n",
+    "        \"ETF\",\n",
+    "        \"Nikko AM SGD Investment Grade Corporate Bond ETF traded on SGX\",\n",
+    "        \"Bond (Singapore)\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"TLT\",\n",
+    "        \"US Treasury Bonds\",\n",
+    "        \"ETF\",\n",
+    "        \"iShares 20+ Year Treasury Bond ETF\",\n",
+    "        \"Bond (US)\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"^GSPC\",\n",
+    "        \"S&P 500\",\n",
+    "        \"Index\",\n",
+    "        \"Tracks 500 largest U.S. companies\",\n",
+    "        \"Equity Index\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"^DJI\",\n",
+    "        \"Dow Jones\",\n",
+    "        \"Index\",\n",
+    "        \"Tracks 30 large U.S. companies, price-weighted\",\n",
+    "        \"Equity Index\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"MSFT\",\n",
+    "        \"Microsoft\",\n",
+    "        \"Stock\",\n",
+    "        \"U.S. tech giant, member of all major U.S. indexes\",\n",
+    "        \"Equity\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"GOOGL\",\n",
+    "        \"Google (Alphabet)\",\n",
+    "        \"Stock\",\n",
+    "        \"Class A shares of Alphabet Inc., parent company of Google\",\n",
+    "        \"Equity\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"NVDA\",\n",
+    "        \"Nvidia\",\n",
+    "        \"Stock\",\n",
+    "        \"Leading GPU and AI chipmaker\",\n",
+    "        \"Equity\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"AAPL\",\n",
+    "        \"Apple\",\n",
+    "        \"Stock\",\n",
+    "        \"Largest U.S. company by market cap\",\n",
+    "        \"Equity\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"AMZN\",\n",
+    "        \"Amazon\",\n",
+    "        \"Stock\",\n",
+    "        \"E-commerce and cloud computing giant\",\n",
+    "        \"Equity\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"TSLA\",\n",
+    "        \"Tesla\",\n",
+    "        \"Stock\",\n",
+    "        \"Electric vehicle and energy company\",\n",
+    "        \"Equity\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"NFLX\",\n",
+    "        \"Netflix\",\n",
+    "        \"Stock\",\n",
+    "        \"Streaming and media company\",\n",
+    "        \"Equity\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"META\",\n",
+    "        \"Meta (Facebook)\",\n",
+    "        \"Stock\",\n",
+    "        \"Parent company of Facebook, Instagram, WhatsApp\",\n",
+    "        \"Equity\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"BTC-USD\",\n",
+    "        \"Bitcoin\",\n",
+    "        \"Crypto\",\n",
+    "        \"Decentralized digital currency\",\n",
+    "        \"Cryptocurrency\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"ETH-USD\",\n",
+    "        \"Ethereum\",\n",
+    "        \"Crypto\",\n",
+    "        \"Smart contract and decentralized app platform\",\n",
+    "        \"Cryptocurrency\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"SOL-USD\",\n",
+    "        \"Solana\",\n",
+    "        \"Crypto\",\n",
+    "        \"High-performance blockchain with low transaction fees\",\n",
+    "        \"Cryptocurrency\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"DOGE-USD\",\n",
+    "        \"Dogecoin\",\n",
+    "        \"Crypto\",\n",
+    "        \"Meme-inspired coin that gained traction\",\n",
+    "        \"Cryptocurrency\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    # [\n",
+    "    #     \"GOLDAMGBD228NLBM\",\n",
+    "    #     \"LBMA Gold Price (USD)\",\n",
+    "    #     \"Index\",\n",
+    "    #     \"Daily gold price per troy ounce in USD from the London Bullion Market (FRED)\",\n",
+    "    #     \"Commodity Index\",\n",
+    "    #     True,\n",
+    "    #     False,\n",
+    "    # ],\n",
+    "    [\n",
+    "        \"GLD\",\n",
+    "        \"SPDR Gold Shares\",\n",
+    "        \"ETF\",\n",
+    "        \"Tracks the price of gold bullion, one of the largest and most traded gold ETFs\",\n",
+    "        \"Commodity ETF\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"IAU\",\n",
+    "        \"iShares Gold Trust\",\n",
+    "        \"ETF\",\n",
+    "        \"Low-cost ETF that tracks the price of gold, similar to GLD\",\n",
+    "        \"Commodity ETF\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"SGOL\",\n",
+    "        \"Aberdeen Standard Physical Gold Shares ETF\",\n",
+    "        \"ETF\",\n",
+    "        \"Physically-backed gold ETF with gold stored in Switzerland\",\n",
+    "        \"Commodity ETF\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "    [\n",
+    "        \"PHYS\",\n",
+    "        \"Sprott Physical Gold Trust\",\n",
+    "        \"Trust\",\n",
+    "        \"Closed-end trust that invests in physical gold bullion\",\n",
+    "        \"Commodity Trust\",\n",
+    "        True,\n",
+    "        False,\n",
+    "    ],\n",
+    "]\n",
+    "columns = [\n",
+    "    \"Symbol\",\n",
+    "    \"Name\",\n",
+    "    \"Type\",\n",
+    "    \"Description\",\n",
+    "    \"Asset Class\",\n",
+    "    \"FinanceDataReader\",\n",
+    "    \"stooq.com\",\n",
+    "]\n",
+    "assets_pdf = pd.DataFrame(assets, columns=columns)\n",
+    "assets_pdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "044e8d84",
+   "metadata": {},
+   "source": [
+    "# Config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "82117da5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Symbol                                              TLT\n",
+       "Name                                  US Treasury Bonds\n",
+       "Type                                                ETF\n",
+       "Description          iShares 20+ Year Treasury Bond ETF\n",
+       "Asset Class                                   Bond (US)\n",
+       "FinanceDataReader                                  True\n",
+       "stooq.com                                         False\n",
+       "Name: 6, dtype: object"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class Config:\n",
+    "    folder = \"stock_data/\"\n",
+    "    write = True  # Options: True, False\n",
+    "    overwrite = False  # Options: True, False\n",
+    "\n",
+    "\n",
+    "Config.overwrite\n",
+    "\n",
+    "assets_pds = assets_pdf.iloc[6]\n",
+    "assets_pds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57196de0",
+   "metadata": {},
+   "source": [
+    "# Download the historical data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "eae73901",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Downloading ^IXIC data...\n",
+      "'stock_data/^IXIC' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading ^NDX data...\n",
+      "'stock_data/^NDX' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading QQQ data...\n",
+      "'stock_data/QQQ' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading ONEQ data...\n",
+      "'stock_data/ONEQ' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading QQQM data...\n",
+      "'stock_data/QQQM' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading A35.SI data...\n",
+      "'stock_data/A35.SI' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading TLT data...\n",
+      "'stock_data/TLT' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading ^GSPC data...\n",
+      "'stock_data/^GSPC' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading ^DJI data...\n",
+      "'stock_data/^DJI' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading MSFT data...\n",
+      "'stock_data/MSFT' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading GOOGL data...\n",
+      "'stock_data/GOOGL' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading NVDA data...\n",
+      "'stock_data/NVDA' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading AAPL data...\n",
+      "'stock_data/AAPL' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading AMZN data...\n",
+      "'stock_data/AMZN' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading TSLA data...\n",
+      "'stock_data/TSLA' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading NFLX data...\n",
+      "'stock_data/NFLX' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading META data...\n",
+      "'stock_data/META' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading BTC-USD data...\n",
+      "'stock_data/BTC-USD' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading ETH-USD data...\n",
+      "'stock_data/ETH-USD' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading SOL-USD data...\n",
+      "'stock_data/SOL-USD' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading DOGE-USD data...\n",
+      "'stock_data/DOGE-USD' data already exists. So, not downloading.\n",
+      "----------------------------------------\n",
+      "Downloading GLD data...\n",
+      "Downloading data from FinanceDataReader...\n",
+      "Downloaded 'GLD' data from FinanceDataReader\n",
+      "2004-11-18 00:00:00 to 2025-05-09 00:00:00\n",
+      "----------------------------------------\n",
+      "Downloading IAU data...\n",
+      "Downloading data from FinanceDataReader...\n",
+      "Downloaded 'IAU' data from FinanceDataReader\n",
+      "2005-01-28 00:00:00 to 2025-05-09 00:00:00\n",
+      "----------------------------------------\n",
+      "Downloading SGOL data...\n",
+      "Downloading data from FinanceDataReader...\n",
+      "Downloaded 'SGOL' data from FinanceDataReader\n",
+      "2009-09-09 00:00:00 to 2025-05-09 00:00:00\n",
+      "----------------------------------------\n",
+      "Downloading PHYS data...\n",
+      "Downloading data from FinanceDataReader...\n",
+      "Downloaded 'PHYS' data from FinanceDataReader\n",
+      "2010-02-26 00:00:00 to 2025-05-09 00:00:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "folder = Config.folder\n",
+    "# Create the folder if it doesn't exist\n",
+    "if not os.path.exists(folder):\n",
+    "    os.makedirs(folder)\n",
+    "    print(f\"Created folder: {folder}\")\n",
+    "        \n",
+    "for i in range(len(assets)):\n",
+    "    print (\"-\"*40)\n",
+    "    assets_pds = assets_pdf.iloc[i]\n",
+    "\n",
+    "    symbol = assets_pds[\"Symbol\"]\n",
+    "    print(f\"Downloading {symbol} data...\")\n",
+    "    data = None\n",
+    "\n",
+    "    file_exists = os.path.exists(f\"{folder}{symbol}.csv\")\n",
+    "    # If the file exists and overwrite is False, print a message and exit\n",
+    "    if file_exists and not Config.overwrite:\n",
+    "        # Print a message indicating that the file already exists\n",
+    "        # and that the user should set overwrite to True to overwrite it\n",
+    "        # or delete the existing file\n",
+    "        print(f\"'{folder}{symbol}' data already exists. So, not downloading.\")\n",
+    "    else:\n",
+    "        if assets_pds[\"FinanceDataReader\"]:\n",
+    "            print(\"Downloading data from FinanceDataReader...\")\n",
+    "            data = fdr.DataReader(symbol, start=\"1990\")\n",
+    "            data = data.reset_index().rename(columns={\"index\": \"Date\"})\n",
+    "            data.drop(columns=[\"Volume\", \"Adj Close\"], inplace=True)\n",
+    "            data.sort_values(by=\"Date\", ascending=True, inplace=True)\n",
+    "            if Config.write:\n",
+    "                data.to_csv(f\"{folder}{symbol}.csv\", index=False)\n",
+    "                if Config.overwrite:\n",
+    "                    print(f\"Downloaded '{symbol}' data from FinanceDataReader and Overwritten\")\n",
+    "                else:\n",
+    "                    print(f\"Downloaded '{symbol}' data from FinanceDataReader\")\n",
+    "            print (f\"{data[\"Date\"].min()} to {data[\"Date\"].max()}\")\n",
+    "        elif assets_pds[\"stooq.com\"]:\n",
+    "            print(\"Downloading data from stooq.com...\")\n",
+    "            url = f\"https://stooq.com/q/d/l/?s={symbol}&i=d\"\n",
+    "            data = pd.read_csv(url)\n",
+    "            if len(data) == 0:\n",
+    "                print(f\"No data found for {symbol} on stooq.com\")\n",
+    "            else:\n",
+    "                data.drop(columns=[\"Volume\"], inplace=True)\n",
+    "                if Config.write:\n",
+    "                    data.to_csv(f\"{folder}{symbol}.csv\", index=False)\n",
+    "                    if Config.overwrite:\n",
+    "                        print(f\"Downloaded '{symbol}' data from stooq.com and Overwritten\")\n",
+    "                    else:\n",
+    "                        print(f\"Downloaded '{symbol}' data from stooq.com\")\n",
+    "                print (f\"{data[\"Date\"].min()} to {data[\"Date\"].max()}\")\n",
+    "        else:\n",
+    "            msg = f\"'{symbol}' data source not specified.\"\n",
+    "            raise ValueError(msg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9d0b8f59",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1990-01-02</td>\n",
+       "      <td>0.605903</td>\n",
+       "      <td>0.616319</td>\n",
+       "      <td>0.598090</td>\n",
+       "      <td>0.616319</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1990-01-03</td>\n",
+       "      <td>0.621528</td>\n",
+       "      <td>0.626736</td>\n",
+       "      <td>0.614583</td>\n",
+       "      <td>0.619792</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Date      Open      High       Low     Close\n",
+       "0 1990-01-02  0.605903  0.616319  0.598090  0.616319\n",
+       "1 1990-01-03  0.621528  0.626736  0.614583  0.619792"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def fetch_stock_daily(symbol, start_date, end_date):\n",
+    "    try:\n",
+    "        data = pd.read_csv(f\"{folder}{symbol}.csv\", parse_dates=[\"Date\"])\n",
+    "        # data = data[(data[\"Date\"] >= start_date) & (data[\"Date\"] <= end_date)]\n",
+    "        # data.set_index(\"Date\", inplace=True)\n",
+    "        # data = data[\"Close\"]\n",
+    "        return data\n",
+    "    except FileNotFoundError:\n",
+    "        print(f\"File {folder}{symbol}.csv not found.\")\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "data = fetch_stock_daily(\"MSFT\", \"2020-01-01\", \"2023-10-01\")\n",
+    "data.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5879114d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       False\n",
+       "1       False\n",
+       "2       False\n",
+       "3       False\n",
+       "4       False\n",
+       "        ...  \n",
+       "8900    False\n",
+       "8901    False\n",
+       "8902    False\n",
+       "8903    False\n",
+       "8904    False\n",
+       "Name: Date, Length: 8905, dtype: bool"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(data[\"Date\"] >= \"2020-05-18\") & (data[\"Date\"] <= \"2020-05-20\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "306156fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.8999078128548517"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TEMP DELETE\n",
+    "((20435 / 10000) ** (1 / 25) - 1) * 100"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "return-on-investment",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/stock_data/QQQM.csv

@@ -0,0 +1,1150 @@
+Date,Open,High,Low,Close
+2020-10-13,121.08000183105469,121.97000122070312,120.55999755859375,121.05000305175781
+2020-10-14,121.33000183105469,121.71900177001953,119.36900329589844,119.97000122070312
+2020-10-15,118.02999877929688,119.27999877929688,117.78900146484375,119.10700225830078
+2020-10-16,119.9000015258789,120.41000366210938,118.5,118.5
+2020-10-19,119.83000183105469,119.83000183105469,116.23500061035156,116.44999694824219
+2020-10-20,116.91000366210938,118.08599853515625,116.29000091552734,116.88999938964844
+2020-10-21,116.94999694824219,118.0,116.66999816894531,117.04000091552734
+2020-10-22,117.11000061035156,117.3489990234375,115.45999908447266,116.72000122070312
+2020-10-23,116.98999786376953,117.0,115.80999755859375,117.0
+2020-10-26,116.01000213623047,116.9800033569336,113.8489990234375,115.18000030517578
+2020-10-27,115.62000274658203,116.44999694824219,115.13999938964844,116.08999633789062
+2020-10-28,114.27999877929688,114.27999877929688,111.5,111.69000244140625
+2020-10-29,112.16000366210938,114.66899871826172,112.0009994506836,113.51000213623047
+2020-10-30,112.69999694824219,113.01000213623047,109.69000244140625,110.86000061035156
+2020-11-02,111.6500015258789,112.23999786376953,109.69999694824219,110.9000015258789
+2020-11-03,111.70999908447266,113.62999725341797,111.30999755859375,113.43099975585938
+2020-11-04,116.44999694824219,118.552001953125,115.97000122070312,117.79000091552734
+2020-11-05,120.51000213623047,121.29900360107422,120.0199966430664,120.91999816894531
+2020-11-06,120.62999725341797,121.22000122070312,119.07499694824219,121.0
+2020-11-09,122.20999908447266,122.70999908447266,118.5,118.5
+2020-11-10,117.1500015258789,117.68000030517578,115.35099792480469,116.31700134277344
+2020-11-11,117.47000122070312,119.19999694824219,117.47000122070312,119.11000061035156
+2020-11-12,119.4800033569336,119.94000244140625,118.0,118.41999816894531
+2020-11-13,119.19000244140625,119.61799621582031,118.3030014038086,119.55000305175781
+2020-11-16,119.45999908447266,120.49099731445312,119.02999877929688,120.06999969482422
+2020-11-17,120.55999755859375,120.62999725341797,119.86000061035156,120.05999755859375
+2020-11-18,119.86000061035156,120.47000122070312,119.40799713134766,119.5479965209961
+2020-11-19,118.97000122070312,120.12999725341797,118.80999755859375,120.12999725341797
+2020-11-20,120.06999969482422,120.29900360107422,119.38999938964844,119.38999938964844
+2020-11-23,119.7699966430664,120.16999816894531,118.36000061035156,119.30000305175781
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requirements.txt

@@ -5,4 +5,5 @@ plotly==5.19.0
 python-dotenv==1.0.1
 numpy==1.26.4
 requests==2.31.0
-beautifulsoup4==4.12.3
+beautifulsoup4==4.12.3
+finance-datareader==0.9.96